Active object recognition or active vision (or as called in robotics, active sensing/localization/navigation) deals with a specific object or scene, searching for the next action, operator, or viewpoint, to optimize some objective function. Although these topics are intimately related to feature selection in machine learning, two key issues raised in the latter field have not been consciously considered by the former, namely, the necessity of an induction algorithm, and the possibility of complex feature interactions (e.g., in-class dependencies).
As a result, an active vision system based on ad hoc heuristics may fail to fully reveal potential feature contributions. For example, most existing systems implicitly assume feature independence (which translates to viewpoint independence for object recognition using an active camera). However, in many cases two or more views are required to discriminate one class of objects from others.
Much research in active vision and robotics has used similar heuristics for active selection of best features. Some techniques include using reduction of entropy to guide the selection of viewpoints, optimal sensor parameter selection for iterative state estimation in static systems by maximizing mutual information, and information gain-based selection of “imaging operators”, taking into account also operation costs. However, none of the above techniques formally addresses the role of an induction algorithm for feature analysis as well as the issue of feature interaction.
Feature selection for classification has recently also been very active. Feature selection is essentially a search for the most sensitive feature subset for the purpose of improved classification accuracy and a significantly reduced feature set. However, existing feature selection paradigm does not deal with a specific test input or case-in-question along with a context. Furthermore, many known feature selection techniques do not use an induction algorithm for feature analysis or address the issue of feature interaction.
One traditional class of feature selection techniques uses a filter model that treats feature selection solely as a preprocessing step for later induction algorithm design. Recent feature selection techniques use a wrapper model that performs cross validation using an induction algorithm on the training set. There have also been efforts to link these two models. However, these algorithms are not directly applicable for conditional feature sensitivity analysis.
For example, the wrapper approach relies on cross validation but oftentimes sufficient training samples do not exist to cross-validate in the neighborhood defined by the context—especially when more than a few features have been measured; on the other hand, most variants of the filter approach do not address the context issue, and often ignore the induction algorithm altogether. Consulting an induction algorithm is necessary during the course of feature evaluation, because the most sensitive feature is not necessarily the one that leads to the most variability in labels (which may lead to minimal empirical error on the training data but large error on test data); the best feature shall lead to the most systematic and predictable variability in labels. The present invention combines the essence of both the wrapper model and the filter model and puts an explicit emphasis on the modeling of contextual features.
For example during an echocardiograph exam, the number of possible measurements is in the hundreds, but a typical echocardiograph exam in the United States only contains about ten different measurements on average. The appropriate selection of additional measurements requires extensive training and field experience and is therefore subjective and error-prone. It would be very helpful if a machine could provide context-sensitive real-time guidance as to what additional feature(s) should be measured for the current case. A feature sensitivity analysis module also provides a way to reduce medical costs by identifying a minimal number of measurements that need to be performed to provide a proper medical diagnosis.